GLIOMATCH: The malignant Glioma immuno-oncology matchmaker: towards data-driven precision medicine using spatially resolved radio-multiomics

GLIOMATCH：恶性胶质瘤免疫肿瘤学媒人：利用空间分辨的放射多组学实现数据驱动的精准医学

• 批准号: 10113516
• 金额: $ 65.57万
• 依托单位: UNIVERSITY OF EDINBURGH
• 依托单位国家: 英国
• 项目类别: EU-Funded
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=10113516
• 关键词: GLIOMATCH; malignant; Glioma; immuno; oncology

Adult and paediatric malignant glioma (GBM and pHGG) remain among the most difficult-to-treat cancers with 5-year survival ratesof <5% despite intensive standard-of-care therapy. The differences among patients and the heterogeneous and plastic nature of eachindividual tumour have resulted in all therapeutic clinical trials failing during the past 20 years. Recently, immunotherapy has beenshowing great promise, but only in subsets of patients. Identifying those patients cannot be done a priori as biomarkers are still largelymissing, nor are we able to follow-up on therapeutic efficacy when patients get treated. The GLIOMATCH project aims at improvingthe clinical outcome of GBM/pHGG patients by enabling immunology-based patient stratification to empower personalised matchingof appropriate immunotherapy, while improving follow-up of clinical responses to existing/novel therapeutics. This will be achievedby integrating spatially resolved, multi-layered tissue maps (using integrated single-cell multiomics), with non-invasive MRI images.This integration will fuel into a novel MRI Radio-multiomics hub, that will be made available to clinical professionals through whichthey can perform tumour-host based patient stratification and personalised therapy matching while interpreting longitudinal follow-upand treatment efficacy. The proposed data-driven models will be developed by analysing the largest cohort of immuno-oncology (I/O)treated GBM/pHGG patients (n>300, including pre-post treatment samples) with matched controls (n>300) and exceptionally long-termsurviving GBM patients (n~140), in which various tumour-host niches will be studied in how they respond to I/O perturbations andlead to improved clinical outcome. This will be empowered by deploying an UNCAN-compatible data lake, to which incremental datacollection will be used to further refine the machine learning models, while proposing novel treatment options. This action is part of theCancer Mission cluster of projects on “Understanding (tumour-host interactions)".

成人和小儿恶性神经胶质瘤（GBM和PHGG）仍然是最难治疗的癌症之一，5年生存率<5％所需的强化标准护理疗法。患者之间的差异以及每种肿瘤的异质性和可塑性的差异导致过去20年中所有治疗性临床试验失败。最近，免疫疗法表现出巨大的希望，但仅在患者子集中。由于生物标志物仍然在很大程度上被遗漏，因此无法先验地确定这些患者，因此在患者接受治疗时，我们也无法跟进治疗效果。 Gliomatch项目旨在通过促进基于免疫学的患者分层来改善GBM/PHGG患者的临床结果，以增强适当的免疫疗法的个性化匹配，同时改善对现有/新颖治疗的临床反应的随访。这将是通过整合空间分辨的多层组织图（使用集成的单细胞多MIC）和非侵入性MRI图像来实现的。这种整合将加入一个新型的MRI Radio-M-Roadimimics Hub中，这将在临床专业人员提供基于肿瘤的患者治疗效率的同时对临床专业人员提供，同时可以进行型疗法的疗程，同时进行了良好的疗程。 The proposed data-driven models will be developed by analysis the largest cohort of immuno-oncology (I/O)treated GBM/pHGG patients (n>300, including pre-post treatment samples) with matched controls (n>300) and exceptionally long-termsurviving GBM patients (n~140), in which various tumour-host niches will be studiod in how they respond to I/O perturbations andlead to改善临床结果。这将通过部署不兼容的数据湖来赋予这一功能，该湖将使用增量数据汇总来进一步完善机器学习模型，同时提出新颖的治疗选择。此行动是“理解（肿瘤 - 宿主相互作用）”项目的癌症任务集群的一部分。